In an interview with the “LogTech Podcast,” industry expert André Kaeber presents a series of provocative theories that can serve as early warning indicators for systemic problems in any SaaS company. He argues that many companies are trapped in silos, clinging to outdated models, and that the biggest hurdle to real progress is not budget, but their own mindset. This article distills seven of these uncomfortable truths that every executive should know.

1. Thesis: Your growth problem is not a sales problem, but a system error.

When the numbers aren’t right, the sales department is often the first to be blamed. Kaeber exposes this as dangerous short-sightedness. The problem is not weak sales, but a company that operates in silos. Sales, marketing, product, and management work in isolation instead of questioning the entire “target operating model.” Weak sales are rarely the cause, but usually only the symptom. The fatal consequence of this misdiagnosis: measures such as laying off the sales team only treat the symptom, waste valuable resources, and further distance the company from a real solution.

It is almost never just one issue or problem in one department! Often, it is not the “right” skills that are needed. I have frequently seen companies rely on people who have been successful in SaaS before, but who lack the basic tools to be successful. They are highly specialized, but not in the areas that the company currently needs!

For managers, this means that an honest audit does not begin with the sales dashboard, but rather with the organizational chart, existing skills, and lines of communication between departments.

2nd thesis: The great productivity lie – more systems, fewer results.

One of the most critical diagnoses is that productivity in many industries has stagnated or even declined over the past 20 years! And this despite the introduction of countless software solutions such as TMS, WMS, and MES. Kaeber calls this “apparent optimization.” This phenomenon reveals a massive misallocation of capital: enormous sums have been poured into technology that has not achieved any measurable increase in productivity. As an example, he cites a WMS/TMS solution whose implementation costs rose from 650 to 1,500 person-days, just to stay technologically up to date without improving core functionality. It is precisely this capital, which flowed into maintaining the status quo, that is now lacking for the disruptive innovations that threaten these companies today. We are increasingly dealing with a kind of swarm stupidity! Just because my CIO/CDO colleague at Company XYZ does it this way doesn’t mean it’s right!

Companies must therefore ask themselves whether their IT budgets are financing innovation or merely perpetuating the costs of a missed transformation. In the future, the specialist department will need to be involved much earlier on!

3rd thesis: Riding dead horses – The danger of technological ignorance.

Many SaaS companies are clinging to business models that are on the verge of obsolescence due to developments such as AI. They are “riding a dead horse,” according to Kaeber, trying to keep a fundamentally outdated system alive through incremental improvements. The key here is to realize that it’s not about optimizing existing processes, but having the courage to throw them completely overboard. This requires the radical honesty to admit that your own business model has an expiration date.

The strategic question is not how to improve the existing system, but whether we have the courage to shut it down before the market does.

4. Thesis: The biggest hurdle is not money, but mindset.

Contrary to popular belief, it is not a lack of capital that is the biggest obstacle to innovation, but rather a rigid mindset. According to Kaeber, in Germany in particular, a mentality characterized by security and safety prevents bold, forward-looking decisions. There is a lack of “future managers” – people who are not concerned with managing the status quo, but with the challenges of logistics in 2035. The technology for the next leap forward is often available, but the collective mindset prevents its implementation.

Technologically, we are ready for new developments in many areas, but our mindset does not allow it because our systems are characterized by security.

Managers must therefore first question their own risk tolerance and that of their organization before discussing technological roadmaps.

5. Thesis: The ego trap – Why founders are often their own worst enemies.

The maturity of a founder is not demonstrated by being able to do everything, but by recognizing when the company needs a different type of leadership than he himself can provide.

6. Thesis: Speed is the new currency – from months to weeks.

Long implementation times are a clear warning sign in the modern SaaS world. A provider who cannot demonstrate their solution within weeks rather than months is no longer competitive. Kaeber introduces the crucial but often overlooked concept of “redundancies”: How quickly can a customer switch from one service provider to another within the software? Today, this flexibility is more important than an endless list of features. The most important lever for software providers is not the development of the next feature, but the radical reduction of implementation time and the increase of interchangeability within the system.

The crucial question for every SaaS buyer is no longer “What can your software do?” but “How quickly can I achieve added value and, more importantly, how quickly can I switch if I need to?”

7. Thesis: Trust beats metrics – The underestimated power of the community.

In an uncertain and rapidly changing technological landscape, trust is one of the most important currencies. It cannot be generated in the short term through marketing metrics, but must grow organically over time. Building communities and thought leadership is therefore a valuable long-term strategy, but one that is often neglected in the German logtech scene. Genuine community building is based on personal contact and authentic give-and-take – in contrast to pure “popcorn cinema” formats, where consumption is purely passive.

In an era of AI-generated arbitrariness, an authentic community becomes the only competitive advantage that cannot be copied.

The message is clear: real progress does not come from the next software update or a new sales strategy, but requires a fundamental change in thinking, culture, and strategic direction. It’s about asking uncomfortable questions and honestly assessing whether current efforts are merely serving to keep alive a system that has already had its day.

So the crucial question is not whether you will adopt the next technology, but rather: Are you prepared to get off your dead horse before it’s too late?

The post 7 uncomfortable truths for SaaS companies (and why your growth problem isn’t in sales) first appeared on André Kaeber.

My first encounters with a transport management system (TMS) date back more than 20 years, to 2003 to be precise. At that time, I was working as a consultant in the field of SAP Supply Chain Execution, primarily for industrial companies, i.e., shippers. There, I became familiar with all aspects of transport management: from inbound processes, including imports, to outbound scenarios, to complex multi-site networks in which strategic, tactical, and operational transport planning had to be closely interlinked.

My focus on the operational, holistic management of transports was particularly influential. It was not just a matter of planning individual transports or calculating freight costs, but rather of taking an end-to-end view: How do I integrate the warehouse, how do I manage the supply to a plant, how do container cycles work, how do I optimize loading space, how are machines, assemblies, and equipment transported optimally, and how do I keep service levels and costs under control? These questions shaped my everyday work and continue to influence my thinking about TMS to this day.

From today’s perspective, the SAP R/3 functionalities at that time were limited, but they had one decisive advantage: they could be implemented quickly, were highly integrated, were sufficient for many companies, and could also be expanded selectively with a little programming. We were often able to implement functioning solutions within a few weeks, sometimes even days. For many shippers, this was a real competitive advantage because they could bring transparency and standardization to their transports with manageable effort.

The broader context

While I base my experiences on projects with shippers, Graham Parker paints a picture of the global TMS evolution in his article “Why Global Shipping’s Brain Needs a Total Rewrite” (2025). He recalls that TMS emerged in the 1990s as an extension of ERP systems, driven by the explosive growth of global trade. SAP was a pioneer in this field, with Oracle following soon after.

Parker describes the years 2005 to 2015 as a kind of “supercycle” for TMS implementations. Systems were introduced worldwide because global supply chains were becoming increasingly complex, customers were demanding greater transparency and speed, regulatory requirements were increasing, and costs were coming under greater pressure.

While Parker examines this development from a macro perspective, looking at global trade flows, BCO shippers, and international trade lanes, I have witnessed the micro perspective of shippers: the operational reality in industry and trade. This is where companies did not plan their transports theoretically, but had to actually manage them day after day across plants, countries, and modes of transport.

Classic TMS: Useful, but no longer sufficient

From a shipper’s perspective, traditional TMS systems represented a huge step forward at the time. For the first time, it was possible to view and manage transports holistically. However, the limitations soon became apparent:

• Interfaces were cumbersome, real-time information was hardly available.
• Lack of web capabilities and collaboration options (e.g., time slot management or WEB EDI)
• Multi-site planning was only possible with considerable customization.
• Workflows were often too rigid to respond flexibly to changes.
• No strategic components in location planning or freight contracts
• Limitation to transport execution vs. planning (lack of heuristics or optimization solutions)
• Statische Stammdaten (Routen & Entfernungsnetzwerke) anstatt dynamischer Routen etc.

Today, 20 years later, many shippers still use these old systems, often with add-ons! But the world has changed: supply chains are more global, volatile, and complex than ever before. Data-driven decisions and automation are a must, not an option!

Power of transformation

The forces at work today, which I sense in my practice not only among shippers:

1. The natural upgrade cycle

Many TMSs introduced during the supercycle phase are now technologically obsolete. Shippers are faced with a decision: continue working with outdated systems or take the plunge into the new.

2. The SaaS (R)evolution

Cloud-native systems enable genuine collaboration across locations, regions, and partners for the first time. For shippers, this means a consolidated view of inbound, outbound, and import shipments in real time. However, many systems are rather limited from the shipper’s perspective, as they are specialized, for example, only for trucks, hardly ever for rail, and often there is a misunderstanding between shipping and transport management!

3. The API and AI Revolution

API-first approaches and AI agents are radically changing the logic of transport management. Shippers can orchestrate their transports across different plants, carriers, and regions without having to operate a monolithic system. However, companies often still lack the knowledge to develop realistic strategies for the future!

Agentic AI Workflows – the next step

This is precisely where my observations over the past few months come in: for me, agentic AI is the logical next step in development.

• Freight cost verification is handled by AI, which instantly compares invoices, rates, and services.
• Transportation execution is dynamic: agents automatically tender, adjust routes, communicate independently with carriers, and manage exceptions.
• Transportation planning becomes a continuous learning process that adapts to disruptions in real time and optimizes scenarios across multiple locations.
• Communication with logistics partners using dynamic interfaces, LLM that communicate with the driver by voice, and AI-supported mapping and interface monitoring!

This changes the role of the TMS: it is no longer a system that maps workflows, but a platform orchestrated by agents.

My assessment – lessons learned from projects

If there’s one thing I’ve learned from over 20 years of TMS projects with shippers, it’s this: technology alone is not enough.

My answer to the question of whether we still need traditional TMS is as follows:

• Technologically: No! Modular, AI-native services are superior.
• Organizational: Not all companies are ready to hand over operational responsibility to autonomous systems, so the transformation alone will actually take years!

The biggest hurdle lies not in IT, but in organization and culture. Trust in agents, governance, and change management are crucial.

Conclusion: Two perspectives, one goal

My review of the shipper world and Parker’s global analysis lead to the same conclusion: we are at a turning point in transport management.

• Classic TMSs have played their part and have been crucial to the progress made over the last two decades.
• But the future belongs to an orchestrated, modular, and AI-driven world in which agents take over operational control and humans focus on strategy, partnerships, and innovation.

The question is not whether we will take this step. The question is how quickly we are prepared to let go of yesterday’s technology and shape the world of tomorrow.

Sources

• Parker, Graham (2025): Why Global Shipping’s Brain Needs a Total Rewrite, Building the next generation of BCO Shipper Supply Chain Software, 18. August 2025.
• Over 20 years of hands-on experience with SAP Supply Chain Execution at industrial and commercial enterprises (inbound, outbound, import, multi-site, operational control).
• Gartner (2024): Emerging AI Workflows in Enterprise Applications.

The post From SAP R/3 to Agentic AI Workflow – My journey through the evolution of transport management first appeared on André Kaeber.